Neoepitopes arise from somatic mutations in the tumor
Data generation from mouse tumor models
- Two cell lines (CT26 colon carcinoma and 4T1 breast cancer)
- Two organs (spleen and tumors)
- With and without checkpoint inhibitor treatment
Method
- Roadmap of data analysis in the code in the github repository
Results
Response filtering
- Response selection criteria: logfold change > 2 and p-value < 0.01
Expression level and rank score
- Now selection criteria is rank < 2% and expression > 0.1 TPM - top 500 neoepitopes
- Can these selection critereia be optimized
Mutation consequence
- Can mutation type be used to find immunugenic neoepitopes?
Elution, binding affinity rank scores and self-similarity
Improved binding affinity
Mutation position
Modelling
- 4 sample of data set
R package and shiny app
We have developed barcc package 
And a shiny app for rapid visualization of the data 
Discussion
- Data set is to small to see a clear pattern in immnunugenic and non-immunugenic neoepitopes
- Also shown by a poor performance of the model
- But! With a trend that if we take into account multiple neoepitope characteristics, we can improve immunogenic selection
Further work:
- Try out with more data, when more peptides are screened
- Is the results the same for human data
- Make packages for human data